Extended Data Fig. 4: Comparison of the PET/MRI and PET/MRITOP classifiers on the preclinical test data. | Nature Biomedical Engineering

Extended Data Fig. 4: Comparison of the PET/MRI and PET/MRITOP classifiers on the preclinical test data.

From: Quantification of intratumoural heterogeneity in mice and patients via machine-learning models trained on PET–MRI data

Extended Data Fig. 4

Column-wise, left to right: H&E, A-Casp-3 and MT histology of six representative test set tumours, and the corresponding phenotypic maps predicted by the PET/MRI and PET/MRITOP classifiers. The PET/MRITOP model was trained using the combined training data of the eight most relevant PET/MRI features, shown in Fig. 4g. The top, middle and bottom pairs of the tumours belong to the test Control, Th-24 and Th-72 groups, respectively. The phenotypic maps are joint probability maps that were colour-coded based on the tumour tissue class colour map shown in the key. Thus, the trained classifiers probabilistically assigned each voxel in the phenotypic maps to either one of the three phenotypic classes. The colour-coded arrows in the histology indicate the respective phenotypic class. The image in the first inset shows a patch of the Control tumour containing viable tumour tissue and necrosis. The second inset illustrates bundles of viable tumour cells distributed within a largely apoptotic region of the Th-24 tumour. Lastly, the image in the third inset shows the highly complex microenvironment of the Th-72 tumour.

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